Data Analysis of Middle School Students’ Mental Health Test in Intelligent Security Management System

Traditional methods of collecting student mental health data are difficult to effectively analyze and process the collected data, and often require manual sorting and statistics, greatly reducing work efficiency. Through the intelligent security management system, the trend of changes in student mental test data was analyzed to predict possible mental health problems, and corresponding intervention measures were taken to prevent the occurrence of problems. This article took a certain university as an example, based on the intelligent security management system, used data mining related technologies to explore the potential mental information of students, and timely presented it to mental counseling staff. At the same time, in response to the different characteristics of association algorithms and decision tree algorithms in data mining, the algorithms were applied to different applications in mental health management systems. After 3 months, the proportion of students with a mental health score of failing was 15.86%, a decrease of 13.41% compared to the proportion of students with an unadjusted mental health score of failing. The result is to provide more intelligent and personalized mental health services for college teachers and students, which is conducive to cultivating and improving the physical and mental health level of students.